Errors, blunders, and lies
Subtitle: how to tell the difference
By: David S. Salsburg
Series: ASA-CRC series on statistical reasoning in science and society
Publisher: Chapman and Hall/CRC
Published: 2017
Language: eng
Number of pages: 154
ISBN-13: 9781498795784; 9781138726987
Subjects: Statistical methods; Science; Methodology; Scientific Errors; Missing observations (Statistics)
Description: We live in a world that is not quite 'right'. The central tenet of statistical inquiry is that Observation = Truth + Error because even the most careful of scientific investigations have always been bedevilled by uncertainty. Our attempts to measure things are plagued with small errors. Our attempts to understand our world are blocked by blunders. And, unfortunately, in some cases, people have been known to lie. In this follow-up to his acclaimed best-seller, The Lady Tasting Tea, David Salsburg opens a door to the widespread use of statistical methods by looking at historical examples of errors, blunders and lies from areas as diverse as archaeology, law, economics, medicine, psychology, sociology, Biblical studies, history, and war-time espionage. In doing so, he shows how, upon closer statistical investigation, errors and blunders often lead to useful information. And how statistical methods have been used to uncover falsified data. Beginning with Edmund Halley's examination of the Transit of Venus and ending with discussions of how many tanks Rommel had during the Second World War and whether modern African censuses contain falsified data, the author invites the reader to come along on this easily-accessible and fascinating journey of how to identify the nature of errors, minimize the effects of blunders, and figure out who the liars are.
Notes: Includes bibliographical references and index.
Table of contents: title: The transit of Venus; title: Probability versus likelihood; title: The central limit conjecture; title: Measuring disease; title: Other uses of multilinear models; title: When multilinear regression is not adequate; title: Correlation versus causation; title: Regression and big data; title: Contaminated distributions; title: The Princeton robustness study; title: When the blunder is what you want; title: Parsing "blunders"; title: The reigns of kings; title: Searching for the "real" Davy Crockett; title: Detecting falsified counts; title: Uncovering secrets; title: Errors, blunders, or curbstoning?